---
title: "Climate Change Dashboard"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
---

```{r setup, include=FALSE}
library(dygraphs)
library(xts)
library(lubridate)
library(dplyr)
library(tidyr)
library(leaflet)
library(sp)
library(raster)
library(mapview)
library(leafsync)
library(glue)

#author: Wanda Bodnar

## Data sources: 
#https://climate.metoffice.cloud/dashboard.html,
#https://www.metoffice.gov.uk/pub/data/weather/uk/climate/datasets/Tmean/date/UK.txt,
#https://www.metoffice.gov.uk/pub/data/weather/uk/climate/stationdata/heathrowdata.txt,
##https://www.kaggle.com/datasets/noahx1/london-weather-2000-2023,
#https://psmsl.org/data/, 
#https://www.sciencedirect.com/science/article/pii/S0079661121000112?via%3Dihub#s0180, 
```

Global
=====================================

Column {data-width=550}
-------------------------------------
### Carbon-dioxide concentration

```{r}
mauna <- read.csv(url("https://climate.metoffice.cloud/formatted_data/co2_Mauna%20Loa%20CO2.csv"))
mauna$Year <- as.numeric(mauna$Year)
mauna <- mauna %>%
  group_by(Year) %>%
  dplyr::summarize(Mean_Mauna = mean(Mauna.Loa.CO2..ppm., na.rm = TRUE))

NOAA <- read.csv(url("https://climate.metoffice.cloud/formatted_data/co2_NOAA%20CO2.csv"))
NOAA$Year <- as.numeric(NOAA$Year)
NOAA <- NOAA %>%
  group_by(Year) %>%
  dplyr::summarize(Mean_NOAA = mean(NOAA.CO2..ppm., na.rm = TRUE))

WDCGG <- read.csv(url("https://climate.metoffice.cloud/formatted_data/co2_WDCGG%20CO2.csv"))
WDCGG$Year <- as.numeric(WDCGG$Year)
WDCGG <- WDCGG %>%
  group_by(Year) %>%
  dplyr::summarize(Mean_WDCGG = mean(WDCGG.CO2..ppm., na.rm = TRUE))

carbon <- merge(mauna, NOAA, by = "Year", all = T)

carbon <- merge(carbon, WDCGG, by = "Year")

names(carbon)[names(carbon) == "Mean_Mauna"] <- "Mauna Loa"
names(carbon)[names(carbon) == "Mean_NOAA"] <- "NOAA"
names(carbon)[names(carbon) == "Mean_WDCGG"] <- "WDCGG"

dygraph(carbon, main = "") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(3, "Set3")) %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyAxis("y", label = "Parts per million (ppm)") %>%
  dyRangeSelector() 
```

### Global mean temperature difference from 1850-1900

```{r}
HadCRUT5 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/gmt_HadCRUT5.csv"))
HadCRUT5$Year <- as.numeric(HadCRUT5$Year)

NOAA <- read.csv(url("https://climate.metoffice.cloud/formatted_data/gmt_NOAAGlobalTemp.csv"))
NOAA$Year <- as.numeric(NOAA$Year)

berkeley <- read.csv(url("https://climate.metoffice.cloud/formatted_data/gmt_Berkeley%20Earth.csv"))
berkeley$Year <- as.numeric(berkeley$Year)

temp <- merge(HadCRUT5, NOAA, by = "Year", all = T)

temp <- merge(temp, berkeley, by = "Year", all = T)

temp <- subset(temp, select= -c(HadCRUT5.uncertainty, NOAAGlobalTemp.uncertainty, Berkeley.Earth.uncertainty))

names(temp)[names(temp) == "HadCRUT5..degC."] <- "Met Office"
names(temp)[names(temp) == "NOAAGlobalTemp..degC."] <- "NOAA"
names(temp)[names(temp) == "Berkeley.Earth..degC."] <- "Berkeley Earth"

dygraph(temp, main = "") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(3, "Dark2")) %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyAxis("y", label = "Temperature (°C)") %>%
  dyRangeSelector() 
```   

Row {data-height=500}
-------------------------------------

### Global ocean heat content difference from 1981-2010

```{r}
EN.4.2.2.c14 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_EN.4.2.2.c14.csv"))
EN.4.2.2.c14$Year <- as.numeric(EN.4.2.2.c14$Year)

Levitus <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_Levitus.csv"))
Levitus$Year <- as.numeric(Levitus$Year)

EN.4.2.2.c13 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_EN.4.2.2.c13.csv"))
EN.4.2.2.c13$Year <- as.numeric(EN.4.2.2.c13$Year)

EN.4.2.2.g10 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_EN.4.2.2.g10.csv"))
EN.4.2.2.g10$Year <- as.numeric(EN.4.2.2.g10$Year)

EN.4.2.2.l09 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_EN.4.2.2.l09.csv"))
EN.4.2.2.l09$Year <- as.numeric(EN.4.2.2.l09$Year)

IAP <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_IAP.csv"))
IAP$Year <- as.numeric(IAP$Year)

JMA <- read.csv(url("https://climate.metoffice.cloud/formatted_data/ohc_JMA.csv"))
JMA$Year <- as.numeric(JMA$Year)

oceanheat <- merge(EN.4.2.2.c14, Levitus, by = "Year", all = T)

oceanheat <- merge(oceanheat, EN.4.2.2.c13, by = "Year", all = T)

oceanheat <- merge(oceanheat, EN.4.2.2.g10, by = "Year", all = T)

oceanheat <- merge(oceanheat, EN.4.2.2.l09, by = "Year", all = T)

oceanheat <- merge(oceanheat, IAP, by = "Year", all = T)

oceanheat <- merge(oceanheat, JMA, by = "Year", all = T)

oceanheat <- subset(oceanheat, select= -c(EN.4.2.2.c14.uncertainty, 
                                        Levitus.uncertainty,
                                        EN.4.2.2.c13.uncertainty,
                                        EN.4.2.2.g10.uncertainty,
                                        EN.4.2.2.l09.uncertainty,
                                        IAP.uncertainty,
                                        JMA.uncertainty))

names(oceanheat)[names(oceanheat) == "EN.4.2.2.c14..10ZJ."] <- "EN.4.2.2.c14"
names(oceanheat)[names(oceanheat) == "Levitus..10ZJ."] <- "Levitus"
names(oceanheat)[names(oceanheat) == "EN.4.2.2.c13..10ZJ."] <- "EN.4.2.2.c13"
names(oceanheat)[names(oceanheat) == "EN.4.2.2.g10..10ZJ."] <- "EN.4.2.2.g10"
names(oceanheat)[names(oceanheat) == "EN.4.2.2.l09..10ZJ."] <- "EN.4.2.2.l09"
names(oceanheat)[names(oceanheat) == "IAP..10ZJ."] <- "IAP"
names(oceanheat)[names(oceanheat) == "JMA..10ZJ."] <- "JMA"

dygraph(oceanheat, main = "") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(7, "Dark2")) %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyAxis("y", label = "10<sup>22</sup> Joules") %>%
  dyRangeSelector() 
```

### Global sea level difference from 1981-2010

```{r}
palmer <- read.csv(url("https://climate.metoffice.cloud/formatted_data/sea_level_lt_Palmer%20et%20al.%202021.csv"))
palmer$Year <- as.numeric(palmer$Year)

CW2011 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/sea_level_lt_CW2011.csv"))
CW2011$Year <- as.numeric(CW2011$Year)

HA2015 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/sea_level_lt_HA2015.csv"))
HA2015$Year <- as.numeric(HA2015$Year)

DA2019 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/sea_level_lt_DA2019.csv"))
DA2019$Year <- as.numeric(DA2019$Year)

FR2020 <- read.csv(url("https://climate.metoffice.cloud/formatted_data/sea_level_lt_FR2020.csv"))
FR2020$Year <- as.numeric(FR2020$Year)

sealevel <- merge(palmer, CW2011, by = "Year", all = T)

sealevel <- merge(sealevel, HA2015, by = "Year", all = T)

sealevel <- merge(sealevel, DA2019, by = "Year", all = T)

sealevel <- merge(sealevel, FR2020, by = "Year", all = T)

sealevel <- subset(sealevel, select= -c(Palmer.et.al..2021.uncertainty, 
                                          CW2011.uncertainty,
                                          HA2015.uncertainty,
                                          DA2019.uncertainty,
                                          FR2020.uncertainty))

names(sealevel)[names(sealevel) == "Palmer.et.al..2021..mm."] <- "Palmer et al. 2021"
names(sealevel)[names(sealevel) == "CW2011..mm."] <- "CW2011"
names(sealevel)[names(sealevel) == "HA2015..mm."] <- "HA2015"
names(sealevel)[names(sealevel) == "DA2019..mm."] <- "DA2019"
names(sealevel)[names(sealevel) == "FR2020..mm."] <- "FR2020"

dygraph(sealevel, main = "") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(5, "Set2")) %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyAxis("y", label = "Milimetres (mm)") %>%
  dyRangeSelector() 
``` 


United Kingdom & London
=====================================

Column {data-width=550}
-------------------------------------
### UK annual mean air temperature
```{r}

setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data")

uk <- read.csv("uk_temp.csv")

dygraph(uk, main = "") %>% 
  dySeries("ann", label = "°C") %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyAxis("y", label = "Temperature (°C)") %>%
  dyLegend(show = "always", hideOnMouseOut = T) %>%
  dyRangeSelector() 
```

### Great Britain sea level from tide gauges
```{r}

setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data/sea level")

sea <- read.csv("uk2.csv")

dygraph(sea, main = "") %>% 
  dySeries("mm", label = "mm", color = "lightblue") %>%
  dyAxis("y", label = "Sea level (mm)") %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.8) %>%
  dyLegend(show = "always", hideOnMouseOut = T) %>%
  dyRangeSelector() 
```

Row {data-height=500}
-------------------------------------

### London annual maximum and minimum air temperature
```{r}
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data")

london <- read.csv("london.csv")

london_monthly_combined <- london %>%
  group_by(year) %>%
  summarise(across(c(tmax, tmin), mean, na.rm=TRUE))

dygraph(london_monthly_combined, main = "") %>% 
  dySeries("tmax", label = "Max temperature", color = "red") %>%
  dySeries("tmin", label = "Min temperature", color = "blue") %>%
  dyAxis("y", label = "Temperature (°C)") %>%
  dyLegend(show = "always", hideOnMouseOut = T) %>%
  dyRangeSelector() 
```

### London annual mean precipitation
```{r}
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data")

london <- read.csv("london.csv")

london_monthly_combined <- london %>%
  group_by(year) %>%
  summarise(across(c(rain_mm), mean, na.rm=TRUE))

dygraph(london_monthly_combined, main = "") %>% 
  dySeries("rain_mm", label = "mm", color = "darkblue") %>%
  dyAxis("y", label = "Rain (mm)") %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyLegend(show = "always", hideOnMouseOut = T) %>%
  dyRangeSelector() 
```

### London annual mean wind speed
```{r}
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data")

wind <- read.csv("wind.csv")

wind_combined <- wind %>%
  group_by(Year) %>%
  summarise(across(c(wspd), mean, na.rm=TRUE))

dygraph(wind_combined, main = "") %>% 
  dySeries("wspd", label = "km/h", color = "green") %>%
  dyAxis("y", label = "Wind speed (km/h)") %>%
  dyOptions(fillGraph = TRUE, fillAlpha = 0.1) %>%
  dyLegend(show = "always", hideOnMouseOut = T) %>%
  dyRangeSelector() 
```


# Temperature projections - Thames Estuary area

Column {data-width=550}
-------------------------------------

### Path: if global average temperature reaches 2°C by 2100

```{r}
## 2
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data/UK-CRI/Temperature/2")
temp2 <- sf::read_sf("2.geojson") 

bins2 <- c(0.8, 1, 1.2, 1.4)
pal2 <- colorBin(palette = c("#ffc700", "#f9b700", "#f3a700", "#ec9700"), domain = c(min(temp2$y2036), max(temp2$y2076)), bins = bins2)


label30_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2036}<br/>") %>%
  lapply(htmltools::HTML)

label40_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2046}<br/>") %>%
  lapply(htmltools::HTML)

label50_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2056}<br/>") %>%
  lapply(htmltools::HTML)

label60_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2066}<br/>") %>%
  lapply(htmltools::HTML)

label70_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2076}<br/>") %>%
  lapply(htmltools::HTML)

label80_2 <- glue(
  "<b>Borough: </b> {temp2$location}<br/>",
  "<b>Average temp: </b> {temp2$y2086}<br/>") %>%
  lapply(htmltools::HTML)

leaflet(temp2) %>%
  addTiles() %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(label = label30_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2036), group = "2030s") %>%
  addPolygons(label = label40_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2046), group = "2040s") %>%
  addPolygons(label = label50_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2056), group = "2050s") %>%
  addPolygons(label = label60_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2066), group = "2060s") %>%
  addPolygons(label = label70_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2076), group = "2070s") %>%
  addPolygons(label = label80_2, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal2(y2086), group = "2080s") %>%
  leaflet::addLegend(pal = pal2, values = c(min(temp2$y2036), max(temp2$y2086)), 
            opacity = 0.7, title = "Median change", position = "bottomleft", labFormat = labelFormat(suffix = "°C")) %>% 
  addLayersControl(
    overlayGroups = c("2030s", "2040s", "2050s", "2060s", "2070s", "2080s"),
    options = layersControlOptions(collapsed = F)) %>%
   hideGroup(c("2040s", "2050s", "2060s", "2070s", "2080s"))
```


### Path: if global average temperature reaches 3°C by 2100

```{r}
## 3
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data/UK-CRI/Temperature/3")
temp3 <- sf::read_sf("3.geojson") 

bins3 <- c(1, 1.2, 1.4, 1.6, 1.8, 2, 2.2)
pal3 <- colorBin(palette = c("#f9b700", "#f3a700", "#ec9700", "#e58700", "#dd7800",
                             "#d56800", "#d56800"), domain = c(min(temp3$y2036), max(temp3$y2086)), bins = bins3)


label30_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2036}<br/>") %>%
  lapply(htmltools::HTML)

label40_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2046}<br/>") %>%
  lapply(htmltools::HTML)

label50_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2056}<br/>") %>%
  lapply(htmltools::HTML)

label60_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2066}<br/>") %>%
  lapply(htmltools::HTML)

label70_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2076}<br/>") %>%
  lapply(htmltools::HTML)

label80_3 <- glue(
  "<b>Borough: </b> {temp3$location}<br/>",
  "<b>Average temp: </b> {temp3$y2086}<br/>") %>%
  lapply(htmltools::HTML)

leaflet(temp3) %>%
  addTiles() %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(label = label30_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2036), group = "2030s") %>%
  addPolygons(label = label40_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2046), group = "2040s") %>%
  addPolygons(label = label50_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2056), group = "2050s") %>%
  addPolygons(label = label60_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2066), group = "2060s") %>%
  addPolygons(label = label70_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2076), group = "2070s") %>%
  addPolygons(label = label80_3, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal3(y2086), group = "2080s") %>%
  leaflet::addLegend(pal = pal3, values = c(min(temp3$y2036), max(temp3$y2086)), 
            opacity = 0.7, title = "Median change", position = "bottomleft", labFormat = labelFormat(suffix = "°C")) %>% 
  addLayersControl(
    overlayGroups = c("2030s", "2040s", "2050s", "2060s", "2070s", "2080s"),
    options = layersControlOptions(collapsed = F)) %>%
  hideGroup(c("2040s", "2050s", "2060s", "2070s", "2080s"))
```

### Path: if global average temperature reaches 4°C by 2100

```{r}
## 4
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data/UK-CRI/Temperature/4")

temp4 <- sf::read_sf("4.geojson") 

bins4 <- c(1, 1.2, 1.4, 1.6, 1.8, 2, 2.2, 2.4, 2.6, 2.8, 3)
pal4 <- colorBin(palette = c("#f9b700", "#f3a700", "#ec9700", "#e58700", "#dd7800",
                             "#d56800", "#d56800", "#cc5800", "#c24700", "#b83600", "#ae2200"), 
                 domain = c(min(temp4$y2036), max(temp4$y2086)), bins = bins4)


label30_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2036}<br/>") %>%
  lapply(htmltools::HTML)

label40_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2046}<br/>") %>%
  lapply(htmltools::HTML)

label50_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2056}<br/>") %>%
  lapply(htmltools::HTML)

label60_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2066}<br/>") %>%
  lapply(htmltools::HTML)

label70_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2076}<br/>") %>%
  lapply(htmltools::HTML)

label80_4 <- glue(
  "<b>Borough: </b> {temp4$location}<br/>",
  "<b>Average temp: </b> {temp4$y2086}<br/>") %>%
  lapply(htmltools::HTML)

leaflet(temp4) %>%
  addTiles() %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(label = label30_4, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2036), group = "2030s") %>%
  addPolygons(label = label40_4, 
             weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2046), group = "2040s") %>%
  addPolygons(label = label50_4, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2056), group = "2050s") %>%
  addPolygons(label = label60_4, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2066), group = "2060s") %>%
  addPolygons(label = label70_4, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2076), group = "2070s") %>%
  addPolygons(label = label80_4, 
              weight = 1,
              opacity = 1,
              color = "white",
              dashArray = "1",
              fillOpacity = 0.7,
              fillColor = ~pal4(y2086), group = "2080s") %>%
  leaflet::addLegend(pal = pal4, values = c(min(temp4$y2036), max(temp4$y2086)), 
            opacity = 0.7, title = "Median change", position = "bottomleft", labFormat = labelFormat(suffix = "°C")) %>% 
  addLayersControl(
    overlayGroups = c("2030s", "2040s", "2050s", "2060s", "2070s", "2080s"),
    options = layersControlOptions(collapsed = F)) %>%
  hideGroup(c("2040s", "2050s", "2060s", "2070s", "2080s"))
```


Row 
-------------------------------------

### Median temperature change averaged across the riparian boroughs

```{r}
setwd("C:/Users/bodna/Documents/Work/Roadmap to Climate Resilience/Working folder/Data/UK-CRI/Temperature")

ave <- read.csv("average.csv")

ave$year <- as.numeric(ave$year)

dygraph(ave, main = "") %>%
  dyAxis("y", label = "Temperature change") %>%
  dySeries("X2_value", label = "2°C", color = "green") %>%
  dySeries("X3_value", label = "3°C", color = "orange") %>%
  dySeries("X4_value", label = "4°C", color = "red") %>%
  dyRangeSelector() 
```

# Rainfall projections - Thames Estuary area